test 1

README.md

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+---
+language: 
+- jav
+pretty_name: Jv Id Asr
+task_categories: 
+- speech-recognition
+tags: 
+- speech-recognition
+---

requirements.txt

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+seacrowd>=0.2.0
+gdown

su_id_asr_split.py

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+import csv
+import os
+from typing import Dict, List
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
+                                       DEFAULT_SOURCE_VIEW_NAME, Tasks)
+
+_DATASETNAME = "su_id_asr"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
+
+_LANGUAGES = ["sun"]
+_LOCAL = False
+_CITATION = """\
+@inproceedings{sodimana18_sltu,
+  author={Keshan Sodimana and Pasindu {De Silva} and Supheakmungkol Sarin and Oddur Kjartansson and Martin Jansche and Knot Pipatsrisawat and Linne Ha},
+  title={{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Frameworks for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}},
+  year=2018,
+  booktitle={Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)},
+  pages={66--70},
+  doi={10.21437/SLTU.2018-14}
+}
+"""
+
+_DESCRIPTION = """\
+Sundanese ASR training data set containing ~220K utterances.
+This dataset was collected by Google in Indonesia.
+"""
+
+_HOMEPAGE = "https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr"
+
+_LICENSE = "Attribution-ShareAlike 4.0 International."
+
+_URLs = {
+    "su_id_asr_train": "https://drive.google.com/file/d/1-9oCkIQSok_STemyNBLx2EDQXfmWabsU/view?usp=sharing",
+    "su_id_asr_dev": "https://drive.google.com/file/d/1IkqEuGrIyKbCSDo9q6F6_r_vkeJ1pcrp/view?usp=sharing",
+    "su_id_asr_test": "https://drive.google.com/file/d/1-7aLW9Tzs4lxm9ImWho91FjpgpVC6wAc/view?usp=sharing",
+}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+def download_from_gdrive(url, output_dir):
+    """Download a file from Google Drive and save it to the specified directory."""
+    file_id = url.split("/d/")[-1].split("/")[0]  # Extract FILE_ID from URL
+    gdrive_url = f"https://drive.google.com/uc?id={file_id}"
+    output_path = os.path.join(output_dir, f"{file_id}.zip")  # Save file
+    gdown.download(gdrive_url, output_path, quiet=False)
+    return output_path
+
+
+class JvIdASR(datasets.GeneratorBasedBuilder):
+    """Javanese ASR training data set containing ~185K utterances."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="su_id_asr_source",
+            version=SOURCE_VERSION,
+            description="su_id_asr source schema",
+            schema="source",
+            subset_id="su_id_asr",
+        ),
+        SEACrowdConfig(
+            name="su_id_asr_seacrowd_sptext",
+            version=SEACROWD_VERSION,
+            description="su_id_asr Nusantara schema",
+            schema="seacrowd_sptext",
+            subset_id="su_id_asr",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "su_id_asr_source"
+
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        
+        def download_from_gdrive(url, name):
+            # Create a temporary directory for downloads
+            with tempfile.TemporaryDirectory() as temp_dir:
+                file_id = url.split("/d/")[-1].split("/")[0]
+                output_path = os.path.join(temp_dir, f"{name}.zip")
+                
+                # Download using gdown with fuzzy=True
+                gdown.download(url, output_path, fuzzy=True)
+                
+                # Use dl_manager to extract and manage the downloaded file
+                extracted_path = dl_manager.extract(output_path)
+                return extracted_path
+
+        # Download and extract all splits
+        paths = {
+            "train": download_from_gdrive(_URLs["su_id_asr_train"], 'asr_sundanese_train'),
+            "dev": download_from_gdrive(_URLs["su_id_asr_dev"], 'asr_sundanese_dev'),
+            "test": download_from_gdrive(_URLs["su_id_asr_test"], 'asr_sundanese_test')
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": paths["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": paths["dev"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": paths["test"]},
+            ),
+        ]
+    
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "text": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_sptext":
+            features = schemas.speech_text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _generate_examples(self, filepath: str):
+        tsv_file = os.path.join(filepath, "asr_sundanese", "utt_spk_text.tsv")
+        with open(tsv_file, "r") as f:
+            tsv_file = csv.reader(f, delimiter="\t")
+            for line in tsv_file:
+                audio_id, sp_id, text = line[0], line[1], line[2]
+                wav_path = os.path.join(filepath, "asr_sundanese", "data", "{}".format(audio_id[:2]), "{}.flac".format(audio_id))
+
+                if os.path.exists(wav_path):
+                    if self.config.schema == "source":
+                        ex = {
+                            "id": audio_id,
+                            "speaker_id": sp_id,
+                            "path": wav_path,
+                            "audio": wav_path,
+                            "text": text,
+                        }
+                        yield audio_id, ex
+                    elif self.config.schema == "seacrowd_sptext":
+                        ex = {
+                            "id": audio_id,
+                            "speaker_id": sp_id,
+                            "path": wav_path,
+                            "audio": wav_path,
+                            "text": text,
+                            "metadata": {
+                                "speaker_age": None,
+                                "speaker_gender": None,
+                            },
+                        }
+                        yield audio_id, ex
+        f.close()